Equipment Engineering Systems (EES) manage the processing of product by manufacturing machines. Yield Management Systems (YMS) utilize parametric and e-test data to analyze yield excursions. Some conventional yield management systems leverage data from a fault detection and classification (FDC) system of conventional equipment engineering systems to provide yield engineers additional data when analyzing yield. In a limited number of conventional yield management systems, the data from the FDC system is used to predict yield. This yield prediction may enable yield engineers to detect potential yield problems before a product has been completed.
One of the biggest problems with technologies that utilize prediction as a cornerstone capability, such as virtual metrology (VM) and yield management enhanced advanced process control (YMeAPC), is the quality of the prediction, especially in the presence of a dynamic prediction environment. There are a number of prediction techniques and prediction adaptation techniques available. Oftentimes, different prediction adaptation techniques work better in different environments. Currently there is no defined way to take advantage of multiple prediction adaptation capabilities in a collaborative fashion to improve prediction quality.
Manufacturing processes are subjected to disturbances and drifts. Wafer-to-wafer (W2W) control of drifting processes requires inline metrology, which adds cost for the metrology station, increases cycle time, and reduces throughput. A trade-off exists between speed of measurements and accuracy. Virtual metrology includes the prediction of metrology variables using information about the state of the process for every wafer. Virtual metrology utilizes FDC data and upstream metrology information for prediction algorithms. Virtual metrology predicts metrology variables (either measurable or non-measurable) based on a state of the process and/or product.
An approach for factory-wide control utilizing virtual metrology is described in the article “Fab-wide Control Utilizing Virtual Metrology,” IEEE Transactions on Semiconductor Manufacturing, Vol. 20, No. 4, November 2007 by Aftab A. Khan, James R. Moyne, and Dawn M. Tilbury. This article “Fab-wide Control Utilizing Virtual Metrology,” which is herein incorporated by reference, discloses a partial least squares (PLS) technique and also double exponentially weighted moving average (dEWMA) controllers that are commonly applied in semiconductor manufacturing processes.
However, prior virtual metrology approaches fail to provide a robust and reusable virtual metrology solution. The current state-of-the-art of virtual metrology includes a number of “one-off” highly customized, non-reusable solutions. These approaches are not very robust over time. Uses are limited due to a lack of understanding of data quality. Prior approaches fail to provide high quality adaptive virtual metrology modeling.